Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 891945, 8 pages
http://dx.doi.org/10.1155/2014/891945
Research Article

A Graphic Method for Identification of Novel Glioma Related Genes

1Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
2State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Jiaotong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, China
3Endoscopy Center, China-Japan Union Hospital of Jilin University, Changchun 130033, China
4Institute of Systems Biology, Shanghai University, Shanghai 200444, China
5CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

Received 18 April 2014; Revised 25 May 2014; Accepted 28 May 2014; Published 23 June 2014

Academic Editor: Tao Huang

Copyright © 2014 Yu-Fei Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. N. Louis, H. Ohgaki, O. D. Wiestler et al., “The 2007 WHO classification of tumours of the central nervous system,” Acta Neuropathologica, vol. 114, pp. 97–109, 2007. View at Google Scholar
  2. T. A. Dolecek, J. M. Propp, N. E. Stroup, and C. Kruchko, “CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005–2009,” Neuro-Oncology, vol. 14, supplement 5, pp. v1–v49, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. D. N. Louis, “The p53 gene and protein in human brain tumors,” Journal of Neuropathology and Experimental Neurology, vol. 53, no. 1, pp. 11–21, 1994. View at Google Scholar · View at Scopus
  4. J. W. Henson, B. L. Schnitker, K. M. Correa et al., “The retinoblastoma gene is involved in malignant progression of astrocytomas,” Annals of Neurology, vol. 36, no. 5, pp. 714–721, 1994. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Guha, M. M. Feldkamp, N. Lau, G. Boss, and A. Pawson, “Proliferation of human malignant astrocytomas is dependent on Ras activation,” Oncogene, vol. 15, no. 23, pp. 2755–2765, 1997. View at Google Scholar · View at Scopus
  6. C. B. Knobbe, A. Trampe-Kieslich, and G. Reifenberger, “Genetic alteration and expression of the phosphoinositol-3-kinase/Akt pathway genes PIK3CA and PIKE in human glioblastomas,” Neuropathology and Applied Neurobiology, vol. 31, pp. 486–490, 2005. View at Google Scholar
  7. A. L. Rinkenbaugh and A. S. Baldwin, “Monoallelic deletion of NFKBIA in glioblastoma: when less is more,” Cancer Cell, vol. 19, no. 2, pp. 163–165, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Chen, W. M. Zeng, Y. D. Cai, K. Y. Feng, and K. C. Chou, “Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities,” PLoS ONE, vol. 7, no. 4, Article ID e35254, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa, “Prediction of drug-target interaction networks from the integration of chemical and genomic spaces,” Bioinformatics, vol. 24, no. 13, pp. i232–i240, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Yamanishi, M. Kotera, M. Kanehisa, and S. Goto, “Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework,” Bioinformatics, vol. 26, no. 12, pp. i246–i254, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Chen, J. Lu, X. Luo, and K.-Y. Feng, “Prediction of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections,” Biochimica et Biophysica Acta: Proteins and Proteomics, vol. 1844, no. 1, pp. 207–213, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Padhy and Y. Gupta, “Drug repositioning: re-investigating existing drugs for new therapeutic indications,” Journal of Postgraduate Medicine, vol. 57, no. 2, pp. 153–160, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Chen, J. Lu, N. Zhang, T. Huang, and Y.-D. Cai, “A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes,” Molecular BioSystems, vol. 10, no. 4, pp. 868–877, 2014. View at Publisher · View at Google Scholar
  14. L. Chen, W.-M. Zeng, Y.-D. Cai, and T. Huang, “Prediction of metabolic pathway using graph property, chemical functional group and chemical structural set,” Current Bioinformatics, vol. 8, no. 2, pp. 200–207, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. H.-W. Ma and A.-P. Zeng, “The connectivity structure, giant strong component and centrality of metabolic networks,” Bioinformatics, vol. 19, no. 11, pp. 1423–1430, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. J. M. Dale, L. Popescu, and P. D. Karp, “Machine learning methods for metabolic pathway prediction,” BMC Bioinformatics, vol. 11, article 15, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Chen, B.-Q. Li, and K.-Y. Feng, “Predicting biological functions of protein complexes using graphic and functional features,” Current Bioinformatics, vol. 8, no. 5, pp. 545–551, 2013. View at Publisher · View at Google Scholar
  18. Y. F. Gao, L. Chen, Y. D. Cai, K. Y. Feng, T. Huang, and Y. Jiang, “Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins,” PLoS ONE, vol. 7, no. 9, Article ID e45944, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Sharan, I. Ulitsky, and R. Shamir, “Network-based prediction of protein function,” Molecular Systems Biology, vol. 3, p. 88, 2007. View at Google Scholar · View at Scopus
  20. K. L. Ng, J. S. Ciou, and C. H. Huang, “Prediction of protein functions based on function-function correlation relations,” Computers in Biology and Medicine, vol. 40, no. 3, pp. 300–305, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. P. Bogdanov and A. K. Singh, “Molecular function prediction using neighborhood features,” IEEE-ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 2, pp. 208–217, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Gao, Q. P. Wang, L. Chen, and T. Huang, “Prediction of human genes' regulatory functions based on proteinprotein interaction network,” Protein and Peptide Letters, vol. 19, no. 9, pp. 910–916, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. L. J. Jensen, M. Kuhn, M. Stark et al., “STRING 8—a global view on proteins and their functional interactions in 630 organisms,” Nucleic Acids Research, vol. 37, no. 1, pp. D412–D416, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. T. H. Gormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Eds., Introduction to Algorithms, MIT Press, Cambridge, Mass, USA, 1990.
  25. J. Davis and M. Goadrich, “The relationship between precision-recall and ROC curves,” in Proceedings of the 23rd International Conference on Machine Learning (ICML '06), pp. 233–240, New York, NY, USA, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. R. Bunescu, R. Ge, R. J. Kate et al., “Comparative experiments on learning information extractors for proteins and their interactions,” Artificial Intelligence in Medicine, vol. 33, no. 2, pp. 139–155, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. D. E. Johnson and G. H. I. Wolfgang, “Predicting human safety: screening and computational approaches,” Drug Discovery Today, vol. 5, no. 10, pp. 445–454, 2000. View at Publisher · View at Google Scholar · View at Scopus
  28. B.-Q. Li, B. Niu, L. Chen et al., “Identifying chemicals with potential therapy of HIV based on protein-protein and protein-chemical interaction network,” PLoS ONE, vol. 8, no. 6, Article ID e65207, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Zhang, M. Jiang, F. Yuan, K. Y. Feng, Y. D. Cai et al., “Identification of age-related macular degeneration related genes by applying shortest path algorithm in protein-protein interaction network,” BioMed Research International, vol. 2013, Article ID 523415, 8 pages, 2013. View at Publisher · View at Google Scholar
  30. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. R. Klein, “Eph/ephrin signaling in morphogenesis, neural development and plasticity,” Current Opinion in Cell Biology, vol. 16, no. 5, pp. 580–589, 2004. View at Publisher · View at Google Scholar · View at Scopus
  32. P. W. Janes, S. Adikari, and M. Lackmann, “Eph/ephrin signalling and function in oncogenesis: Lessons from embryonic development,” Current Cancer Drug Targets, vol. 8, no. 6, pp. 473–489, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. E. B. Pasquale, “Eph receptors and ephrins in cancer: bidirectional signalling and beyond,” Nature Reviews Cancer, vol. 10, no. 3, pp. 165–180, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. L. Ding, G. Getz, D. A. Wheeler et al., “Somatic mutations affect key pathways in lung adenocarcinoma,” Nature, vol. 455, no. 7216, pp. 1069–1075, 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. D. Zagzag, D. R. Friedlander, B. Margolis et al., “Molecular events implicated in brain tumor angiogenesis and invasion,” Pediatric Neurosurgery, vol. 33, no. 1, pp. 49–55, 2000. View at Google Scholar · View at Scopus
  36. P. Pu, Z. Zhang, C. Kang et al., “Downregulation of Wnt2 and β-catenin by siRNA suppresses malignant glioma cell growth,” Cancer Gene Therapy, vol. 16, no. 4, pp. 351–361, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. P. Knizetova, J. Ehrmann, A. Hlobilkova et al., “Autocrine regulation of glioblastoma cell cycle progression, viability and radioresistance through the VEGF-VEGFR2 (KDR) interplay,” Cell Cycle, vol. 7, no. 16, pp. 2553–2561, 2008. View at Google Scholar · View at Scopus
  38. Y. T. Gu, Y. X. Xue, X. Y. Wei, H. Zhang, and Y. Li, “Calcium-activated potassium channel activator down-regulated the expression of tight junction protein in brain tumor model in rats,” Neuroscience Letters, vol. 493, no. 3, pp. 140–144, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. H. Xie, Y. X. Xue, L. B. Liu, and Y. H. Liu, “Endothelial-monocyte-activating polypeptide II increases blood-tumor barrier permeability by down-regulating the expression levels of tight junction associated proteins,” Brain Research, vol. 1319, pp. 13–20, 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. T. Nikuseva-Martic, V. Beros, N. Pecina-Slaus, H. I. Pecina, and F. Bulic-Jakus, “Genetic changes of CDH1, APC, and CTNNB1 found in human brain tumors,” Pathology: Research and Practice, vol. 203, pp. 779–787, 2007. View at Google Scholar
  41. C. Perego, C. Vanoni, S. Massari et al., “Invasive behaviour of glioblastoma cell lines is associated with altered organisation of the cadherin-catenin adhesion system,” Journal of Cell Science, vol. 115, no. 16, pp. 3331–3340, 2002. View at Google Scholar · View at Scopus
  42. The Cancer Genome Atlas Research Network, “Comprehensive genomic characterization defines human glioblastoma genes and core pathways,” Nature, vol. 455, pp. 1061–1068, 2008. View at Publisher · View at Google Scholar
  43. S. A. Rao, A. Arimappamagan, P. Pandey et al., “miR-219-5p inhibits receptor tyrosine kinase pathway by targeting EGFR in glioblastoma,” PLoS ONE, vol. 8, no. 5, Article ID e63164, 2013. View at Publisher · View at Google Scholar · View at Scopus
  44. K. H. Plate, G. Breier, H. A. Weich, and W. Risau, “Vascular endothelial growth factor is a potential tumour angiogenssis factor in human gliomas in vivo,” Nature, vol. 359, no. 6398, pp. 845–848, 1992. View at Publisher · View at Google Scholar · View at Scopus
  45. B. Millauer, L. K. Shawver, K. H. Plate, W. Risau, and A. Ullrich, “Glioblastoma growth inhibited in vivo by a dominant-negative Flk-1 mutant,” Nature, vol. 367, no. 6463, pp. 576–579, 1994. View at Publisher · View at Google Scholar · View at Scopus
  46. S. Koch, S. Tugues, X. Li, L. Gualandi, and L. Claesson-Welsh, “Signal transduction by vascular endothelial growth factor receptors,” The Biochemical Journal, vol. 437, no. 2, pp. 169–183, 2011. View at Publisher · View at Google Scholar
  47. M. J. Karkkainen and T. V. Petrova, “Vascular endothelial growth factor receptors in the regulation of angiogenesis and lymphangiogenesis,” Oncogene, vol. 19, no. 49, pp. 5598–5605, 2000. View at Google Scholar · View at Scopus
  48. B. Jenny, J. A. Harrison, D. Baetens et al., “Expression and localization of VEGF-C and VEGFR-3 in glioblastomas and haemangioblastomas,” Journal of Pathology, vol. 209, no. 1, pp. 34–43, 2006. View at Publisher · View at Google Scholar · View at Scopus
  49. J. N. Anastas and R. T. Moon, “WNT signalling pathways as therapeutic targets in cancer,” Nature Reviews Cancer, vol. 13, no. 1, pp. 11–26, 2013. View at Publisher · View at Google Scholar · View at Scopus
  50. H. Yano, A. Hara, J. Shinoda et al., “Immunohistochemical analysis of β-catenin in N-ethyl-N-nitrosourea- induced rat gliomas: implications in regulation of angiogenesis,” Neurological Research, vol. 22, no. 5, pp. 527–532, 2000. View at Google Scholar · View at Scopus
  51. X. Liu, L. Wang, S. Zhao, X. Ji, Y. Luo, and F. Ling, “β-catenin overexpression in malignant glioma and its role in proliferation and apoptosis in glioblastma cells,” Medical Oncology, vol. 28, no. 2, pp. 608–614, 2011. View at Publisher · View at Google Scholar · View at Scopus
  52. P. Polakis, “Wnt signaling and cancer,” Genes & Development, vol. 14, no. 15, pp. 1837–1851, 2000. View at Google Scholar · View at Scopus
  53. Y. Liu, W. Yan, W. Zhang et al., “MiR-218 reverses high invasiveness of glioblastoma cells by targeting the oncogenic transcription factor LEF1,” Oncology Reports, vol. 28, no. 3, pp. 1013–1021, 2012. View at Publisher · View at Google Scholar · View at Scopus
  54. S. Nada, T. Yagi, H. Takeda et al., “Constitutive activation of Src family kinases in mouse embryos that lack Csk,” Cell, vol. 73, no. 6, pp. 1125–1135, 1993. View at Publisher · View at Google Scholar · View at Scopus
  55. S. Zhang, W. C. Huang, L. Zhang, C. Zhang, F. J. Lowery et al., “SRC family kinases as novel therapeutic targets to treat breast cancer brain metastases,” Cancer Research, vol. 73, pp. 5764–5774, 2013. View at Google Scholar
  56. J. Chen, A. Elfiky, M. Han, C. Chen, and M. W. Saif, “The Role of Src in colon cancer and its therapeutic implications,” Clinical Colorectal Cancer, vol. 13, pp. 5–13, 2014. View at Google Scholar
  57. C. Egan, A. Pang, D. Durda, H. Cheng, J. H. Wang, and D. J. Fujita, “Activation of Src in human breast tumor cell lines: elevated levels of phosphotyrosine phosphatase activity that preferentially recognizes the Src carboxy terminal negative regulatory tyrosine 530,” Oncogene, vol. 18, no. 5, pp. 1227–1237, 1999. View at Publisher · View at Google Scholar · View at Scopus
  58. T. J. Yeatman, “A renaissance for SRC,” Nature Reviews Cancer, vol. 4, no. 6, pp. 470–480, 2004. View at Google Scholar · View at Scopus
  59. M. R. Stettner, W. Wang, L. B. Nabors et al., “Lyn kinase activity is the predominant cellular Src kinase activity in glioblastoma tumor cells,” Cancer Research, vol. 65, no. 13, pp. 5535–5543, 2005. View at Publisher · View at Google Scholar · View at Scopus
  60. C. V. Lund, M. T. Nguyen, G. C. Owens et al., “Reduced glioma infiltration in Src-deficient mice,” Journal of Neuro-Oncology, vol. 78, no. 1, pp. 19–29, 2006. View at Publisher · View at Google Scholar · View at Scopus
  61. M. S. Ahluwalia, J. de Groot, W. M. Liu, and C. L. Gladson, “Targeting SRC in glioblastoma tumors and brain metastases: rationale and preclinical studies,” Cancer Letters, vol. 298, no. 2, pp. 139–149, 2010. View at Publisher · View at Google Scholar · View at Scopus